System and method for using psychological significance pattern information for matching with target information

ABSTRACT

A computer-implemented system for creating a classification significance pattern for end users, and enabling end users to use their classification significance pattern to conduct custom searches for target information, such as information about products, services, and jobs, as well as enabling third parties, such as vendors and potential employers, to target their advertisements to groups of users meeting a certain classification. A classification significance pattern is created by having a user take a psychological test, for example, that includes a personality test, a design taste test, a recreation/travel test, a life satisfaction test, an interactive game module, or a career/job test, and having the system automatically score such test and classifying the user based on a defined abstract classification.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. ProvisionalApplication No. 60/216,469 filed Jul. 6, 2000.

[0002] This application relates to co-pending provisional applications:

[0003] (1) Ser. No. 60/220,398, filed Jul. 24, 2000, titled “A methodand system for a document search system using search criteria comprisedof ratings prepared by experts”;

[0004] (2) Ser. No. 60/215,492, filed Jul. 6, 2000, titled “System andMethod for Anonymous Transaction In A Data Network and User Profiling ofIndividuals Without Knowing Their Real Identity.”;

[0005] (3) Ser. No. 60/252,868, filed Nov. 21, 2000, titled “InteractiveAssessment Tool.”; which are incorporated fully herein by reference.

COPYRIGHT NOTICE

[0006] A portion of this patent document contains material which issubject to copyright protection. The copyright owner has no objection tothe facsimile reproduction by anyone of the patent document or thepatent disclosure, as it appears in the Patent and Trademark Officepatent file or records, but otherwise reserves all copyright rightswhatsoever.

FIELD OF INVENTION

[0007] This invention relates generally to a computer-implemented systemfor creating a psychological, personality, or behavioral significancepattern for end users, more particularly, using such psychologicalsignificance pattern to match users with target information, such asinformation on products, services, and career openings.

BACKGROUND

[0008] Employers and advertisers have used personality profiling fordecades to target specific individuals for specific job functions,products, or services. Recently, there has been an increasing uneaseregarding the use of such psychological tools, especially with respectto liability exposure and invasion of privacy considerations. Thisunease may arise from having third-party companies use personalityprofiles without the consent and/or knowledge of individuals. A tool isdesired that enables individuals to knowingly use their personalsignificance pattern to search for target information, such asinformation on jobs, products, and services, thereby reversing thetraditional control of such profiling data and alleviating thenonconsensual use of such information.

[0009] Search engines, such as Alta Vista, Excite, Webcrawler, and thelike, are available on the Internet. Users typically enter a keyword onthe Web page and the search engine returns a list of documents (e.g.,through hyperlinks) where the keywords may be found. (Individuals andusers herein are used interchangeably.) Depending on several factorssuch as the keywords used, the search engine's algorithms, availableuser related data, and the like, the resulting list may contain hundredsand even thousands of documents. A way to refine a search result, i.e.,shorten the list returned, based on the personal characteristics and/orarchetypes (e.g., “personality”) of a user is highly desirable.

[0010] Targeted marketing of individuals on the Internet is also common.Displayed advertisements or offers may also be keyword-linked, such thatadvertisements indexed or related to certain keywords are displayed onlyif the user enters at least one of those keywords.

[0011] This could be seen, for example, by a user entering a keyword,e.g., “travel,” on a search engine's search box and havingadvertisements related to the keyword “travel,” e.g., books on travel,travel agencies, cruises, and the like, be displayed on the resultingWeb page. Such keyword-linked mechanism, however, does not take intoaccount the personality, behavior, or psychology of a user. (A user'spersonality, behavior, and psychology are herein collectively referredto as “personality”). A way to take into account a user's personality soas to have a more efficient and effective targeted marketing is highlydesirable.

[0012] Targeted marketing conventionally also employs information aboutthe user. Internet service providers (ISPs), for example, monitor userswho are logged into their system. They monitor the user for informationsuch as Web sites visited, purchasing pattern, types of advertisementsclicked, gender, resident address, types of articles read, and the like.Using such information, a profile based on these prior and explicitdeclarations of interest is created for each user such that onlyadvertisements that would likely interest the user are displayed on aWeb page. However, such personal profile information is usually obtainedwithout the consent or knowledge of the user and typically does notadequately predict a user's preference when a new situation occurs, suchas a search for an item that the user has never requested or explicitlyexpressed an interest in before. It is often difficult or impractical toobtain specific preference data for an individual relating to all theproducts, services and information with which that individual may beusefully matched. Thus, a way to efficiently match users with targetinformation (e.g., via a search engine or targeted marketing) which isnot keyword-linked and does not require users to explicitly declare aninterest in that information beforehand, is desired.

[0013] Target information as defined herein includes all informationthat a user may want to do a search on or information that a third partymay want to present (e.g., auditory) or display to a user. It alsoincludes information such as information on products and services,articles, music, logos, advertisements, images, videos, and the like.

[0014] Several patents address targeted marketing and searches on theInternet but none addresses users' control on their significancepatterns enabling them to utilize their user significance patterns tosearch for target information based on their personality. None addressesthe creation of user significance patterns by having users participatein an online psychological test and based on such psychological testtaken, create and maintain classifications and archetypes that would beemployed in matching target information to a particular user, whethersuch matching is a result of a search or targeted marketing. Noneaddresses the creation and maintenance of classifications based oncharacteristics and/or archetypes, typically independent of the contentof the target information and abstracted from independent informationobtained from a psychological test taken, and using such classificationto match information. U.S. Pat. No. 5,848,396 issued to Gerace teaches amethod of targeting audience based on profiles of users, which arecreated by recording the computer activity and viewing habits of theusers. This method is based on the explicitly declared interests ofusers. U.S. Pat. No. 5,835,087 issued to Herz et al. teaches a method ofautomatically selecting target objects, such as articles of interest toa user. The method disclosed in Herz generates sets of search profilesfor the users based on attributes such as the relative frequency ofoccurrence of words in the articles read by the users, and uses thesesearch profiles to identify future articles of interest. This methoddepends on the use of keywords, which also requires an explicitdeclaration of interest from the user.

[0015] European Patent Application EP-A-0718784 describes a system forretrieving information based on a user-defined profile. A server actingon behalf of the client identifies information on the basis of theuser-defined profile, to generate a personalized newspaper which isdelivered to the user. This provides for an automatic sorting of thelarge volume of data available on the World Wide Web to generate asubset of information which is tailored to the user's specific interest.However this system is only used for providing newspaper data to astatic user whose desires may change periodically.

[0016] Traditional marketing methodology often involves makingdeductions of interest based on crude demographic attributes such asage, education level, gender and household income. However, thesemethods of ascertaining user interest in a specific product or serviceare typically very inaccurate and the level of targeting achievablethrough these demographic methods is typically poor. Moreover, some ofthese user attributes (such as education, age, and income) are subjectto change over time. In the present invention, a method is describedwhere the user's cognitive style is abstracted from a set of specificresponses. This is a relatively stable “signature” or significancepattern qualifying an individual's interest in products, services andinformation (i.e., target information) in a fundamental manner. Thissignificance pattern is not based on demographic attributes.

[0017] From the discussion above, it should be apparent that there is aneed for an online psychological patterning system that enables users toclassify themselves based on characteristics and/or archetypes, and touse such characteristics and/or archetypes to obtain or receive targetinformation better suited to their personality. Such a system would havemuch wider applicability than currently used systems, because specificdeclarations of interest through selection of keywords or other similaruser input would not be required for each user. Once the user'scognitive style is ascertained, the user's abstracted significancepattern would be applicable to a variety of foreseen and unforeseensituations over time.

[0018] What is needed is a system where the psychological significancepattern is under the user's control, where the user is classified undera classification that is created through an online psychological test,where the classification is used to match users with target information,and which contains the above features and addresses the above-describedshortcomings in the prior art.

[0019] The methodology for the technical solution to these problemsdescribed hereunder, represents a generic set of procedures for rapidlyanalyzing complex biological data sets and uncovering novelrelationships within them. This innovation is relevant to meeting (a)the general need for new tools to investigate complex systems; and (b)the practical need for shortcuts that will generate useful predictionsfrom complex data, even under the computational constraints of‘point-of-use’ devices.

[0020] Multivariate data derived from a variety of sources, represent avector of measures that describe the state or condition of a particularsubject. Accessing the descriptive and predictive capabilities inherentin these vectors requires the use of powerful but general analytictechniques. Standard statistical analysis packages that contain this“toolbox” of techniques are commercially available (e.g., SAS™, SPSS™,BMDP™), as are an array of texts describing general multivariatetechniques (Johnson, 1998; Sharma, 1996; Tabachnick and Fidell., 1996;Srivastava and Carter, 1983; Romesburg, 1984). However, while supplyingthe basic tools for formal analysis, none of these resourcesspecifically addresses the issues faced when trying to extrapolate fromthese kinds of data to probable outcomes in “real-world, real-time”settings.

[0021] Significant efforts to understand the complexity of dynamicsthese kinds of data provide are presently underway across an array ofscientific disciplines. For example, RNA expression data generated fromgenome-wide expression patterns in the budding yeast S. cerevisiae, wereused by Eisen, et al. (1998) to understand the life cycle of the yeast.They employed a cluster analysis to identify patterns of genomicexpression that appear to correspond with the status of cellularprocesses within the yeast during diauxic shift, mitosis, and heat shockdisruption. The clustering algorithm employed was hierarchical, based onthe average linkage distance method. Similarly, Heyer and colleagues(Heyer et al., 1999) developed a new clustering methodology that theyrefer to as a “jackknifed correlation analysis”, and generated acomplete set of pairwise jackknifed correlations between expressedgenes, which they then used to assign similarity measures and clustersto the yeast genome.

[0022] Applying graph theory to this same kind of problem, Ben-Dor, etal (1999) developed another form of clustering algorithm, which theyeventually applied to similar data. And Tamayo, et al. (1999), Costa andNetto (1999), and Toronen et al. (1999) each approached this kind ofmultivariate problem by developing a series of self-organizing maps(SOMs), a variation on the k-means clustering theme. Tamayo's experienceis illustrative of the point. Microarray data for 6416 human genes weregenerated from four cell lines, each undergoing normal hematopoieticdifferentiation. After applying a variance filter, 1036 genes wereclustered into a 6×4 SOM. These developed into archetypes descriptive ofthe expression patterns roughly associated with cell line and maturationstage.

[0023] Other techniques try to project the problem from the multivariatespace into a series of bivariate ones. Walker, et al. (1999) developed a“Guilt-by-Association” model that in essence reduces a gene-by-tissuelibrary to a matrix of “present” or “absent” calls in a series ofstandard 2×2 contingency tables. In their model, under the assumptionsof the null hypothesis, the “presence” and “absence” calls acrosslibraries for each fixed pair of genes should be distributed as aChi-square. Using Fisher's Exact test, a p-value testing the assumptionof “no association” is then calculated. They decrease theiranalysis-wide false positive rate by applying the appropriate Bonferronicorrection factor to the multiple comparison problem. Applying thistechnique to a set of 40,000 human genes across 522 cDNA libraries, theywere able to identify a number of associations between unidentifiedgenes and those with known links to prostate cancer, inflammation,steroid synthesis and other physiological processes.

[0024] Greller and Tobin (1999) developed a more general approach to thepattern recognition/discrimination problem. They derived a measure ofstatistical discrimination by establishing an analysis that transposesthe clustering question into an outlier detection problem. Assuming auniform distribution of interstate expression, and by accounting forboth a statistical distribution of baseline measures and uncertainty inthe observation technology, they derive a decision function that assignsa subject, in their case a gene, to one of three states: selectivelyupregulated, selectively downregulated, or unchanged. And Brown, et al.(2000) derived a knowledge-based analysis engine based on a techniqueknown as “support vector machines” (SVMs). These “machines” are actuallynonlinear in silico discrimination algorithms that “learn” todiscriminate between, and derive archetypes for, binarially attributeddata.

[0025] Complex biological systems often yield measurements that cannoteasily be analyzed by reductionist means. As new technologies expand therate, scope and precision with which such measurements are made, thereis an accompanying need for new analytical tools with which tounderstand the underlying biological phenomena. Furthermore, ubiquitousaccess to modest computational power (in handheld devices, for instance,or on web client-server systems) has made it possible to imagine a rangeof field applications for such analytical tools, provided they aresimpler and easier to use than more formal statistical packages.Protigen, Inc. (Applicant herein) has been testing the use ofconventional web server-based architectures (accessible through desk-topand wireless handheld devices) for real-time analysis of complexbiological data, consistent with the modest computational overhead thatcan be afforded each simultaneous user in a large web community. Thegoal is to explore the possibility of applying such tools to such areasas the real-time adjustment of online education to a user's cognitive(learning) style, point-of-care serum diagnostics for osteoporoticwomen, and the accurate prediction of a protein's solubility in aheterologous system based on its sequence.

[0026] Those skilled in the art will further recognize the wideapplicability of such methodology to problems in areas ranging frompsychology, knowledge management, artificial intelligence, andtext-searching to cancer and pharmacogenomics. The following citedexample data sets are not intended to limit the scope of the invention:

[0027] 1. Cognitive test and behavioral preference data from a cohort of1373 anonymous online users. The fundamental assumption underlying thistype of psychometric analysis, a staple of personality psychology overthe past fifty years, is that the human mind is a complex biologicalsystem whose state attributes can be reliably measured by self-reports.A second assumption is that these state attributes influence humanbehavior. The results obtained from our preliminary analysis aredescribed in greater detail below.

[0028] 2. Detailed serum biochemistry and 3-year bone mineral densitydata from a cohort of 220 osteoporotic women. A point-of-care diagnosticthat could deduce the rate of aggregate bone loss from multivariateclues provided by the serum levels of insulin-like growth factors,selected binding proteins, and CICP would be invaluable for identifyingpost-menopausal women at high risk of developing complications fromosteoporosis. An exciting possibility is that the relative levels ofthese biochemical markers carry information that cannot be derived fromthe levels themselves.

[0029] 3. Solubility and amino acid sequence data from a set of 180eukaryotic proteins expressed in E. coli as part of a genomics program.The effects of amino acid composition on heterologous protein solubilityhave been investigated by a number of groups (Wilkinson and Harrison,1991; Zhang et al, 1998) but the interaction of a protein's structuraland chemical attributes with a foreign environment appears to bemultivariate in nature and has, so far, eluded all predictivealgorithms. Since less than 30% of any random cDNA sequence will resultin soluble (i.e. assayable) protein when expressed in an E. coli host,even with the use of fusion partners such as thioredoxin, there isbuilt-in inefficiency in any high-throughput screen employing abacterial cell for evaluating eukaryotic collections. An appropriatepre-screen in silico could lower screening costs by a factor of 3 ormore.

[0030] References Cited Above

[0031] Ben-Dor, A., R. Shamir, and Z. Yakhini. 1999. Clustering geneexpression patterns. J. Comp. Biol. 6:281-297.

[0032] Brown, M.P.S., W.N. Grundy, D. Lin, N. Cristianini, C.W. Sugnet,T.S. Furey, M. Ares, D. Hausller. (2000) Knowledge-based analysis ofmicroarray gene expression data using support vector machines. PNAS97:262-267.

[0033] Costa, J. A. and M. L. Netto. (1999). Estimating the number ofclusters in multivariate data by self-organizing maps. Int'l. J. NeuralSyst. 9(3):195-202.

[0034] Eisen, M. B., P. T. Spellman, P. O. Brown, and D. Bottstein.(1998). Cluster analysis and display of genome-wide expression patterns.PNAS 95:14863-14868.

[0035] Greller, L. D. and F. L. Tobin. (1999). Detecting selectiveexpression of genes and proteins. Genome Res. 9:282-305.

[0036] Heyer, L. J., S. Kruglyak, and S. Yooseph. 1999. Exploringexpression data: Identification and analysis of coexpressed genes.Genome Res. 9:1106-1115.

[0037] Johnson, D. E. (1998) Applied Multivariate Methods for DataAnalysis. Duxbury Press. Pp. 567.

[0038] Keller G and Snodgrass R (1999) Human embryonic stem cells: Thefuture is now. Nature Med. 5:151-152.

[0039] Robinson, J. P., Shaver, P. R. and Wrightsman, L. S. eds. In“Measures of Personality and Social Psychological Attitudes”. AcademicPress, San Diego, Calif. 1991.

[0040] Romesburg, H. C. (1984). Cluster Analysis for Researchers.Lifetime Learning Publications. Pp. 334.

[0041] Sharma, S. (1996) Applied Multivariate Techniques John Wiley &Sons. Pp. 493.

[0042] Srivastava, M. S. and E. M. Carter. (1983) An Introduction toApplied Multivariate Statistics. North-Holland. Pp. 394.

[0043] Tabachnick, B. G. and L. S. Fidell. (1996) Using MultivariateStatistics. Harper Collins. Pp. 860.

[0044] Tamayo, P., D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, E.Dmitrovsky, E. S. Lander, and T. R. Golub. (1999). Interpreting patternsof gene expression with self-organizing maps:methods and application tohematopoietic differentiation. PNAS. 96:2907-2912.

[0045] Toronen, P., M. Kolehmaninen, G. Wong, and E. Castren. (1999).Analysis of gene expression data using self-organizing maps. FEBS Lett.451(2): 142-146.

[0046] Walker, M. G., W. Volkmuth, E. Sprinzak, D. Hodgson, and T.Klingler. 1999. Prediction of gene function by genome scale expressionanalysis: prostate cancer-associated genes. Genome. Res. 9:1198-1203.

[0047] Wilkinson, D. and Harrison, R. (1991) Predicting the solubilityof recombinant proteins in Escherichia coli. Bio/Technology 9: 443-448.

[0048] Zhang, Y., Olsen, D., Nguyen, K., Olson, P., Rhodes, E. andMascarenhas, D. (1998) Expression of eukaryotic proteins in soluble formin E. coli. Protein Express. Purif. 12: 159-165.

BRIEF SUMMARY OF THE INVENTION

[0049] What is claimed is a computer implemented method for matching acomputer user with target information by creating a classificationsignificance pattern for the user through the use of a psychologicaltest, by creating a classification index for the target information, andby finding relevant target information for the user by matching one ormore elements of the classification significance pattern to the targetinformation classification index.

[0050] Also claimed are apparatus and computer program products toaccomplish similar purposes.

[0051] A classification significance pattern herein includespsychological, behavioral, personality, or other attributes that may betested, created, and/or maintained by a psychological testing tool for auser. Such classification significance pattern includes, but is notlimited to, the classification of a user into certain characteristicsand/or archetypes or models.

[0052] The invention enables a user to take an online psychologicaltest, have the system automatically score such test, have the systemcreate and/or maintain a classification significance pattern for theuser, such as a classification significance pattern that contains thecharacteristics and/or archetypes measured by the online psychologicaltest, and have the system use such classification significance patternto match users with target information. Optionally, the user may loginto the system and take the online psychological test anonymously, bysupplying a pseudonym (i.e., a fictitious name), such as a user-supplieduser name, and thus, enforcing an additional level of privacy. (To“create a classification significance pattern” herein refers to thecreation and maintenance (updates) of a classification significancepattern.)

[0053] Because users voluntarily take the test and are typically put onnotice, for example, by a notice on the Web page, the issue of privacyand control of the user's classification significance pattern is placedunder the user's control. Furthermore, because the users may log intothe system anonymously by supplying a pseudonym, the issue ofunsolicited marketing communications is alleviated.

[0054] The online psychological test measures various aspects of a user,such as personality, psychology, disposition, behavior and the like.Based on these aspects, classifications are created which are used tomatch the users with target information, such that both the user and thetarget information contain classification information (e.g., fields in adatabase). Furthermore, the target information may be classified, forexample, by characteristics and/or archetypes rather than or in additionto the contents of the electronic information (e.g., having a searchfiltered not only by keywords but also by classifications measured bythe psychological test).

BRIEF DESCRIPTION OF THE DRAWINGS

[0055]FIG. 1. is a diagram illustrating an exemplary architecture of thepresent invention.

[0056]FIG. 2 is a block diagram representation of one of the computersin the system illustrated in FIG. 1.

[0057]FIG. 3 illustrates a high level block diagram showing how a userobtains a classification profile and uses such profile to search forinformation.

[0058]FIG. 4 is an exemplary representation of a user interface enablinga user to enter a response to a question from a psychological test.

[0059]FIG. 5 illustrates a high-level block diagram showing targetedmarketing based on the user's classification profile.

[0060]FIG. 6 illustrates functional block diagrams describing anexemplary set of steps determine a user's personal significance pattern.

[0061]FIG. 7 illustrates a functional block diagram describing anexemplary set of steps in matching the user's significance pattern totarget information.

DETAILED DESCRIPTION OF THE INVENTION

[0062] The following detailed description illustrates the invention byway of example, not by way of limitation of the principles of theinvention. This description will clearly enable one skilled in the artto make and use the invention, and describes several embodiments,adaptations, variations, alternatives and uses of the invention,including what we presently believe is the best mode of carrying out theinvention.

[0063] The invention will be described by way of illustration withreference to a specific psychological testing method, referred to as aPersonality Trait Topography (“PTT”), but it should be understood thatother psychological testing tools and profiling methods may also beemployed in the present invention. Furthermore, although the customerinput and actions described refer to inputs from a keyboard or a mouse,this invention also covers other interfaces such as those using voice ora touch screen. Similarly, the specific computational methods andcorrelation schemes described herein may be replaced with equivalentstatistical methods within the framework and claims of the presentinvention.

System Architecture

[0064]FIG. 1 shows an exemplary system architecture to carry out thepresent invention, including a standard Internet or Intranet web server150 that is capable of sending Web pages and processing scripts, adatabase server 160 that stores and handles database manipulation andupdates, and an application server 180 that contains and executes thelogic embodying the features of the present invention.

[0065] A user (local user 105 or remote user 115, respectively) employstypically a computer containing an Internet browser software 110, 120(or an Internet-enabled appliance) to access and connect to the Webserver 150, database server 160, and application server 180.

[0066] The Web server 150, database server 160, and application server180 are connected to a data network, such as a local area network 130which may also be connected to the Internet through a wide area network(WAN) 140. The Web server is a device, typically a computer, whichcontains a Web server software 152 and scripts 154. Scripts are programsthat contain instructions that may be executed, for example, by a Webserver software. Scripts are typically written using scriptinglanguages, such as JavaScript, Microsoft® VBScript, Microsoft® ActiveServer Page, and Allaire® ColdFusion. Microsoft® Internet InformationServer is an example of a Web server software.

[0067] The database server 160 is a device, typically a computer, whichcontains a database management system (DBMS) software 161, as well asthe data used and/or manipulated in the present invention. Microsoft®SQL Server and Oracle's DBMS products are examples of DBMS software.

[0068] The registration database 162 maintains data on users who haveregistered in the system. It contains fields such as user name,password, demographic information (e.g., zip code), user occupation,household income, education, gender, whether the user has completed thepsychological test, the user's characteristic(s) and/or archetype(s) andthe like. In the preferred embodiment, the user name is a pseudonym thatis user-supplied to provide the user with another level of privacy.Information contained in the registration database 162 (FIG. 1) istypically obtained when the user first registers with the system,however, calculated or derived information, such as the numeric ortextual representation of the user characteristic may also be stored.

[0069] The preferred embodiment of the invention uses a psychologicaltest or trait evaluation method developed by the inventor hereingenerically referred to as “Personality Trait Topography” (PTT). The PTTcomprises a psychometric inventory in which user responses to a set ofquestions are solicited on a seven-point scale. Other embodimentsinclude a number of psychological tests, preferably consisting of apersonality test, a design taste test, a color test, an interactive gamemodule, a recreation/travel test, a life satisfaction test, and acareer/job test. An alternative psychological testing methodology may besubstituted for the PTT.

[0070] The database elements of the preferred PTT are shown in FIG. 1.The personality significance pattern database 163, the design tastedatabase 164, the recreation/travel database 165, the life satisfactiondatabase 166, and the career/job database 167 maintain data on theresponses by and scores of the user in the personality test, the designtaste test, the recreation/travel test, the life satisfaction test, andthe career/job test, respectively.

[0071]FIG. 1 also shows product database 168 maintains data on productsavailable within the system including: the classification of theproducts (compatible with or matching that measured by the psychologicaltest). Fields include product name, description, correlation value, andthe like. This classification is described below in an exemplarydescription with respect to FIG. 8. Relationships are initiallyestablished between archetypes, on the one hand, and behaviors,preferences or attitudes on the other. For example, individuals assignedto archetypes can be polled on the extent of their preferences, orself-reported skills. Using canonical correlation, chi square, and otherappropriate tools well known to a professional statistician, actualnumerical values linking archetypes to an increased (or decreased)affinity for a particular product, activity, behavior or attitude can bederived. Unlike previous methods for measuring personality and style,the PTT generates robust, quantitative and reliable relationshipsbetween archetypes and each of dozens of behavioral and productcategories. Such known relationships can, in turn be used to generatereliable predictions of an individual's disposition to the given items,provided that individual's archetype pattern has first been measuredusing the PTT.

[0072] The service database 169 maintains data on services availablewithin the system, including the classification of the services(compatible with or matching that tested by the psychological test.)Fields include service name, description, correlation value, and thelike.

[0073] The product and service databases (which are examples of targetinformation), as well, as other information database, contain fieldsthat match or are compatible with the classification of the users. Theclassification, such as for the product or service, is typicallydetermined by the supplier of the target information. A user interface,as part of the system, may be provided enabling a supplier of suchinformation to enter or indicate the proper classification, for example,through check boxes, lists, and the like.

[0074] For example, in the preferred embodiment including the PTT, ifthe supplier believes that a product would be of interest to individualswith an “M” (mythic) characteristic or to those with the “A” (artist)archetype, the supplier of information checks these two boxes to have an“M” in the mythic_empiric field and a “Yes” in the artist field bestored in the appropriate databases. This way, the target informationmay be matched with the user's classification profile. The PTT isdescribed in detail below.

[0075] The application sever 180 is a device, typically a computer,which contains certain application software, such as the user interfaceprogram 182, the profiling program 184 (e.g., Brain Terrain), the searchengine 186, and the targeted marketing program 188.

[0076] The user interface program 182 generally comprises program logicthat displays Web pages to users, typically web pages enabling users toregister within the system, take the psychological test, or search thesystem for products or services. In the preferred embodiment, it isemployed using a Web server software in conjunction with scripts.

[0077] The profiling program 184 is a software program that calculatesand creates the user significance pattern by considering user'sresponses to the psychological test and classifying the user, e.g.,based on the characteristics and archetypes measured by thepsychological test.

[0078] The search engine 186 is a software program that enables users tosearch for target information in the system, such as products, services,and employment opportunities, based on the user's significance pattern.It may also provide user interface logic. (Thus, if a user is interestedin a product, the user searches for target information about theproduct.)

[0079] One skilled in the art will recognize that the search algorithmemployed by the Search Engine 186 (FIG. 1) may be employed in a numberof ways. Any search methodology that computes and sorts outcomesaccording to predetermined algorithmic relationships between the PTTpersonal style patterns and behavioral or other outcomes, may be used.The methods by which these algorithmic relationships can be establishedare described hereunder.

[0080] The targeted marketing program 188 is a software program thatcontains logic that determines what advertisement is to be displayed.

[0081] One skilled in the art will recognize that the system describedin FIG. 1 may be implemented in a single computer, where the databaseare stored in computer readable medium, such as in a hard disk drive ora CD ROM, as well as having the user interface described above, notgenerated by a web server software and scripts, but rather displayed andexecuted by a interpretive or compiled programming language such asVisual Basic or C++.

[0082] Furthermore, while the above embodiment illustrates the variouscomponents, such as the web server 150, the database server 160, and theapplication server 180 embodied in an individual device, one of ordinaryskill in the art will realize that the functionality may be distributedover a plurality of computers. One of ordinary skill in the art willalso recognize that the databases defined herein, as well as the fieldsin the database, may be modified, added, or deleted depending forexample, on what psychological test is employed, the information desiredto be stored and monitored, the system and/or implementation design, andthe like. For example, an articles database containing articlesclassified by the characteristics defined in the psychological test maybe added to provide users in the system with articles suited to theirpersonality.

[0083] One skilled in the art will also recognize that the psychologicaltest need not be taken online, but rather the user significance pattern,for example, as a result of a written (non-online) psychological test),may be directly stored into the database, e.g., the registrationdatabase 162 in FIG. 1.

[0084]FIG. 2 is a block diagram of an exemplary computer 200 such asmight comprise any of the servers or computers in FIG. 1. Each computer200 operates under control of a central processor unit (CPU) 1102, suchas a “Pentium™” microprocessor and associated integrated circuit chips,available from Intel Corporation™ of Santa Clara, Calif., USA. Acomputer user can input commands and data from a keyboard and mouse 212and can view inputs and computer output at a display 210. The display istypically a video monitor or flat panel display device. The computer 200also includes a direct access storage device (DASD) 204, such as a fixedhard disk drive. The memory 206 typically comprises volatilesemiconductor random access memory (RAM). Each computer preferablyincludes a program product reader 214 that accepts a program productstorage device 216, from which the program product reader can read data(and to which it can optionally write data). The program product readercan comprise, for example, a disk drive, and the program product storagedevice can comprise removable storage media such as a floppy disk, anoptical CD-ROM disc, a CD-R disc, a CD-RW disc, DVD disk, or the like.Each computer 200 can communicate with the other connected computersover the network 220 through a network interface 208 that enablescommunication over a connection 218 between the network and thecomputer.

[0085] The CPU 202 operates under control of programming steps that aretemporarily stored in the memory 206 of the computer 200. When theprogramming steps are executed, the pertinent system component performsits functions. Thus, the programming steps implement the functionalityof the system components illustrated in FIG. 1. The programming stepscan be received from the DASD 204, through the program product 216, orthrough the network connection 218. The storage drive 204 can receive aprogram product, read programming steps recorded thereon, and transferthe programming steps into the memory 206 for execution by the CPU 202.As noted above, the program product storage device can comprise any oneof multiple removable media having recorded computer-readableinstructions, including magnetic floppy disks, CD-ROM, and DVD storagediscs. Other suitable program product storage devices can includemagnetic tape and semiconductor memory chips. In this way, theprocessing steps necessary for operation in accordance with theinvention can be embodied on a program product.

[0086] Alternatively, the program steps can be received into theoperating memory 206 over the network 218. In the network method, thecomputer receives data including program steps into the memory 206through the network interface 208 after network communication has beenestablished over the network connection 218 by well-known methods thatwill be understood by those skilled in the art without furtherexplanation. The program steps are then executed by the CPU 202 toimplement the processing and features of the present invention.

[0087]FIG. 3 illustrates an exemplary logic flow on how a user uses hisor her own significance pattern to conduct searches. The user first logsonto the system as shown in step 302. The user does this by accessing aweb site using an Internet browser 110, 120 (as shown in FIG. 1),typically by typing the URL address on the Internet browser address boxor by selecting the Web site via a hyperlink. A user who is new to thesystem is asked to register with the system by supplying a user name anda password. The user name and password are stored in the registrationdatabase 162 (FIG. 1). Once the user logs and registers with the system,the user takes the psychological test, as shown in box 304. Thepsychological test may be broken down into a series of mini-tests.

[0088] Once the user completes the psychological test, the profilingprogram 184 (illustrated in FIG. 1) converts each test response by theuser to a raw score points or index. These raw points are furthermanipulated to create a significance pattern represented by numbers andtext. The system then stores the user's significance pattern at step 306in the Registration database 162 (FIG. 1). At any time after thecreation and storing of the significance pattern, the user may usehis/her significance pattern to conduct searches, as shown in box 308,thereby making the significance pattern part of the search criteria.

[0089] For example, a search for the keyword “travel” results in a userinterface or Web page listing tours suited to the user's personality.For example, if the user has been determined to having a personalitythat prefers fixed schedules rather than spontaneity, tours that have anumber of preplanned activities are listed rather than those tours withlittle or minimal preplanned activities or have those non-preferredtours listed last on the list. The classifications of the tours based onthe characteristics and archetypes measured (e.g. Table I and II below)are stored as part of the target information database, e.g., the servicedatabase 169 (FIG. 1).

[0090] Employment matching or searching may also be done. One way ofemploying the features of the present invention is to have supervisorstake a similar psychological test and create a significance pattern forsuch supervisors. Thus, a search for jobs, for example, results in alist of jobs, considering both the user's personality and that of theprospective supervisor.

[0091] Personality Trait Topography

[0092] The preferred psychological testing methodology is the PTT, whichmay be used as follows:

[0093] A field indicating the archetype being measured may be added inthe product, service, or other target information database (such as,employment database). For example, the product or service databasecontains a field called Empiric_Mythic, which is one archetype tested bythe psychological test. (See Table I below). An “M” in this fieldindicates that the product is more suited for users who are “mythic,” a“E” indicates that the product is more suited for users who are“empiric,” and a “” (blank or null string) indicates that the productequally applies regardless of the characteristic.

[0094] In the preferred embodiment, the invention uses a psychologicaltest, herein referred to as “PTT”, which measures severalcharacteristics (listed below in Table I). TABLE I SampleCharacteristics of PTT Index Characteristics 1 OJ (“O” = Open-Ended/“J”= Judgmental) 2 FU (“F” = Focused/“U” = Unfocused) 3 CB (“C” =Concrete/“B” = Abstract) 4 TP (“T” = Territorial/“P” = Pacifist) 5 EM(“E” = Empiric/“M” = Mythic) 6 AG (“A” = Anomic/“G” = Gregarious) 7 IX(“I” = Internal Locus of Control/ “E” = External Locus of Control)

[0095] PTT is conducted by asking a user a set of questions addressingthe chacteristics that are measured. Based on the user's response, theprofiling program 184 (in FIG. 1) then classifies the user.

[0096] 1. Index OJ

[0097] Index OJ measures the novelty-seeking characteristic of a user.Type “O” (open-ended) users consider all decisions to be provisionaland, thus, are constantly reevaluating issues. They do not care much forregimentation, and generally will ignore rules that they deem do notmake sense. Typically, they are spontaneous and are happy to make plansas they go along. Type “J” judgmental) users, on the other hand, aretypically driven by rules, tradition, and formal decision-makingprocesses, and are generally law-abiding. They expect and feelcomfortable with some amount of regimentation and structure in theirlives. They typically plan ahead and feel uncomfortable just ‘playing itby ear.’

[0098] 2. IndexFU

[0099] Type “F” (focused) users (line 2) typically tend to be driven,“one-track-minded,” “goal-oriented,” and intensely focused on theirendeavors. Often they will work for hours, while completely oblivious tosurroundings. They tend to take things seriously, and sometimes, need tolearn to lighten up. Type “U” (unfocused) users, on the other hand, tendto take things lightly. They tend to take frequent breaks while workingand are very conscious of their immediate surroundings and, thus, areeasily distracted from their current work or purpose. They tend to havethe philosophy that having fun is more important than achieving goals.

[0100] 3. Index CB

[0101] Type “C” (concrete) (line 3) users tend to be detail-oriented,tend to be very sensitive to their immediate surroundings, are moreinterested in the details rather than in the big picture. Generally,they have little patience for grand ideas and theories, and are morelikely to focus on the present rather than on the future. Type “B”(abstract) users, on the other hand, tend to easily synthesizeinformation and abstract ideas. Their insights make them excellent“high-altitude” or “big picture” analysts. They usually are goodinventors and are able to easily conceptualize complex systems. Theytend to enjoy reading novels with complicated but ingenious plots andtend to be good at extrapolating to the future.

[0102] 4. Index TP

[0103] Type “T” (territorial) users (line 4) tend to be aggressive, tobe very loyal, to root for the home team, to not value diversity, to bevery team-oriented, and to be fierce competitors. Thus, they will oftenexclude “outsiders.” Type “P” (pacifist) users, on the other hand, tendto look for mediated solutions to conflict and are more willing toconsider rehabilitation than punishment. They tend to be “politicallycorrect,” to be very inclusive of other cultures and ways of life, tohave diverse interests, and to see the planet as an organic whole.

[0104] 5. Index EM

[0105] Type “E” (empiric) users (line 5) are driven primarily by logic,not subject to making emotional decisions as other people, at times,cold and unemotional, methodical and hierarchical in their thinking, andoften very intelligent. They tend to look for the facts of the casebefore making a decision. Type “M” (mythic) users are generallyspiritual, superstitious, and very likely to believe in thesupernatural, in an after-life, or reincarnation. They are likely toconsider the existence of angels and extraterrestrials and believe intheir existence. They tend to be exceptionally receptive to nature, art,and beauty.

[0106] 6. Index AG

[0107] Type “A” (anomic) (line 6) users are often loners and enjoysolitary pursuits. They tend to place a low value on social status,fashion, and chitchat, tend to be independent thinkers and usuallydevelop extremely close relationships with pet animals. Type “G”(gregarious) users, on the other hand, often value their status withintheir own social group, and will tirelessly work to improve theirstanding. They tend to pay great attention to appearances and grooming,and “fitting in” with their friends. They are great to have at partiesand often adopt socially extroverted behaviors, even if this is anunnatural characteristic of their personalities.

[0108] 7. Index IX

[0109] Type “I” (internal locus of control) users (line 7) tend to takeresponsibility for their own actions and the consequences thereof, andgenerally have a better self-esteem than average people. They see theirlives as being under their own control, with the outcome dependent upontheir own actions. Type “X” (external locus of control) users, on theother hand, have low self-esteem, tend to blame luck or some externalauthority for their own failings in life, and tend to seek and oftenmeekly submit to the direction from others. They usually feel a sense ofpowerlessness about their world and feel that they are incapable ofchanging the world to their own advantage.

[0110] Index DH

[0111] “D” types are likely to be socially sophisticated, charming,deceptive, even manipulative. “II” types lack social graces, but tend tobe down-to-earth honest.

[0112] Index RS

[0113] Type “R” is a Risk-Taker. “S” is Security-Conscious.

[0114] Archetypes are derived from the pattern of scores obtained by anindividuals across the above indexes, or scales. Archetypes areheuristic abstractions that can be constructed in a number of ways. Theexample shown below illustrates one method currently in use. Othermethods may be developed in the future. Table II below shows samplearchetypes based on the characteristics listed in Table I. TABLE IISample Archetypes Archetype (Most Frequent Characteristics) AnalysisArtist Favored professions: artist, social worker (M, A, U, O)Disfavored professions: lawyer, entrepreneur Job Performance: poor atdealing with both subordinates and authority Musical tastes:non-traditional forms of music (eclectic) Areas of greatest lifesatisfaction: spiritual life Areas of lowest life satisfaction: income,current job Favorite activities: music, reading, creative pursuitsBanker Favored professions: engineer, scientist, banker (X, J, C, T)Disfavored professions: lawyer, politician Job Performance: good withsuperiors, excellent record-keeping Musical tastes: rock-n-roll, countryAreas of greatest life satisfaction: community, government Areas oflowest life satisfaction: current job, income Favorite activities: musicand reading, social activities Counselor Favored professions:-artist,social worker (P, O, I, M) Disfavored professions: scientist, engineerJob Performance: good at group processes, meetings Musical tastes: jazz,blues Areas of greatest life satisfaction: home/dwelling, career successAreas of lowest life satisfaction: government, local community Favoriteactivities: social activities, creative pursuits Devotee Favoredprofessions: social worker (C, X, P, M) Disfavored professions: lawyer,entrepreneur Job Performance: terrible at dealing with subordinates,great record-keeping Musical tastes: all kinds Areas of greatest lifesatisfaction: family, dwelling, spiritual life Areas of lowest lifesatisfaction: income, available leisure time Favorite activities:outdoor recreation General Favored professions: entrepreneur, engineer(T, B, E, J) Disfavored professions: artist, social worker JobPerformance: excellent with subordinates, hates group process ofdecision-making Musical tastes: rock-n-roll Areas of greatest lifesatisfaction: choice of profession, family, and community Areas oflowest life satisfaction: current job, income Favorite activities:social activities, spectator sports Manager Favored professions:entrepreneur (F, J, G, E) Disfavored professions: artist JobPerformance: excellent dealing with superiors, okay with record-keepingMusical tastes: classical Areas of greatest life satisfaction: careersuccess Areas of lowest life satisfaction: friends, community, availableleisure time Favorite activities: outdoor recreation, sports PoliticianFavored professions: politician, lawyer (I, B, O, M) Disfavoredprofessions: engineer Job Performance: poor record-keeping, poor atdealing with the boss, great with subordinates. Musical tastes: gospel,light classical Areas of greatest life satisfaction: choice ofprofession, career success Areas of lowest life satisfaction: familyrelationships, physical fitness Favorite activities: sailing, music andreading, social activities Trustee Favored professions: lawyer (G, E, F,C) Disfavored professions: artist Job Performance: excellent with bossesand subordinates, poor record-keeping Musical tastes: all kinds Areas ofgreatest life satisfaction: choice of profession, physical fitness Areasof lowest life satisfaction: friends, spiritual life Favoriteactivities: outdoor recreation, spectator sports Soldier Favoredprofessions: engineer (U, E, I, T) Disfavored professions: artist JobPerformance: great at dealing with subordinates, hates meetings Musicaltastes: easy listening, top 40 Areas of greatest life satisfaction:physical fitness, community Areas of lowest life satisfaction: dwelling,family relationships Favorite activities: music and reading

[0115] The PTT, described, herein is also a psychological test thatcreates cognitive user significance patterns that are typically “stable”over time. That is, changeable data such as demographic data, address,phone number, age, etc. is not used.

[0116] One skilled in the art will recognize that other characteristicsand archetypes, including other classification, may be measured anddeveloped to classify users. Furthermore, one skilled in the art willrecognize that other psychological testing methods aside from PTT may beemployed to create a classification that would be used by the system tomatch users with electronic information.

[0117] User Interface and Classification Method

[0118]FIG. 4 is an exemplary representation of a user interface or GUI,such as a Web page, enabling a user to take a psychological test, e.g.,a PTT assessment. The personality test portion of PTT quantifies theuser's personality. The personality test asked a set of questions, towhich the user may respond by choosing one of the displayed options. Forexample, in FIG. 4, the question 402 (“If a leader can't buildconsensus, the policy should be abandoned”) is a sample question testingthe personality of the user. The user responds by clicking on one of theoption boxes (as shown in 404). Each question is scored on a seven-pointscale (−3 to +3) where −3 is “Strongly Disagree” and +3 is “AgreeStrongly,” (with each user's response contributing to the relevantcharacteristic and/or archetype measured, e.g., adding 3 points orsubtracting 3 points). The user's response is then stored in anappropriate user storing database, in this case, the personality profiledatabase 163 (in illustrated in FIG. 1).

[0119] A series of such questions is provided to measure each traitscale, however, unlike many previous psychological inventories, the PTTdoes not limit each question to a single underlying trait. Instead, eachquestion may contribute to multiple scales. The precise contribution toeach scale is hypothesis-based, and may be adjusted empirically untilthe results obtained are consistent. An alternative approach is toperform factor analysis' by traditional statistical methods and then usethe results from such an analysis to assign the scoring matrix.

[0120] The response of the user may be ignored in the calculation of thesignificance pattern depending on traditional measures such as factoranalysis and discriminant analysis. Answers to questions that do notshow a factor analysis, i.e., show a 0.40 correlation coefficient orless, for example, to the desired characteristic, are ignored indetermining or calculating the significance pattern.

[0121] The profiling program 184 (illustrated in FIG. 1) calculates themean and standard deviation for all answers for each user and thennormalizes the answers based on these two numbers, thereby expressing aset of responses as normalized standard deviations. Each response isthen multiplied by an appropriate factor, to generate an aggregate scoreset, representing the significance pattern. Each aggregate score setcontains a score for each characteristic listed in Table 1.

[0122] The aggregate score set is then further normalized by taking theaggregate score set of a suitable large number of users (e.g., more than75), calculating a mean and standard deviation for each type ofaggregate score for each characteristic, and then further normalizingeach user's score for that distribution. The final result is a set ofnormalized aggregate scores expressed as standard deviations, i.e., thescores are normalized within a normalized aggregate score set comparedto the result of each user.

[0123] The profiling program 184 generates a significance pattern or aportion of it based on the user's responses. The significance patternmay also be expressed as a mnemonic string of characters that containsthe three most deviant characteristic scored (normalized aggregatescores), plus an indicator for the strongest correlation to the existingarchetypal patterns.

[0124] For example, in a system including PTT method, a user may becategorized as “MAU9R” meaning he is a “mythic,” “anomic,” and a“unfocused.” The string “9R” means that in a scale of 1 to 10, the useris a 9 in the Artist archetype shown in Table II.

[0125] Referring back to FIG. 3, a search 308 requesting for productswith a “travel” keyword, for example, results in a web page listingproducts that contain an “M” on the Empiric_Mythic field, “A” on theAnomic_Gregarious field, or “U” on the Focused_Unfocused field.

[0126] The design taste psychological test measures the design and tastepreference of the user. The design taste test displays a number ofsketches of house interiors and asks the user's preference by having theuser select one of the options displayed (e.g., “Strongly Dislike,”“Dislike,” “Slightly Dislike,” “Neutral,” “Like Slightly,” “Like,” and“Love It.”). Each question is scored on a seven-point scale (−3 to +3)where −3 is “Strongly Dislike” and +3 is “Love It.”

[0127] The Recreation/Travel survey measures the recreation and travelpreference of the user by asking the user to enter his or her responsein an online survey form. (This online survey form is implemented byusing a Web server software and scripts.) The user, for example, isasked to list the titles of three favorite books, to list the titles offive favorite movies, to list five activities (unrelated to the user'semployment) which the user has spent the most time during the past year,and to list three subjects (unrelated to the user's employment) whichthe user wants to learn more about. The user enters the responses intothe online form and accordingly submits the responses by clicking on the“Submit” button.

[0128] The Life Satisfaction Survey measures the user's satisfactionwith life in general. A set of questions is posed to the user, which theuser responds to by selecting an option box (“Highly Unsatisfied,”“Unsatisfied,” “Slightly Unsatisfied,” “Neutral,” “Slightly Satisfied,”“Satisfied,” and “Very Satisfied.”) Sample questions include: “Howsatisfied are you with your current job?”; “How satisfied are you withyour current choice of profession?”; “How satisfied are you with yourcurrent family income?;” “How satisfied are you with the amount of timeyou have available for recreational activities?”; and the like. Eachquestion is scored on a seven-point scale (−3 to +3) where a score of−3is “Highly Unsatisfied” and +3 is “Very Satisfied.”

[0129] The Jobs/Careers Test measures how compatible a user is with aparticular job. Questions include, for example, “With appropriatetraining, how well do you think you could perform as an accountant orbanker?;” “With appropriate training, how well do you think you couldperform as a scientist?;” “With appropriate training, how well do youthink you could perform as a high school schoolteacher?;” and the like.The user gives his answer by selecting one of the options displayed(e.g., “Extremely Poorly,” “Poorly,” “Somewhat Poorly,” “About Average,”“Moderately Well,” “Well,” and “Extremely Well.”) Each question isscored on a seven-point scale (−3 to +3) where a score of −3 is“Extremely Poorly” and +3 is “Extremely Well.”

[0130] Computation of Personal Style

[0131] In a preferred embodiment, an exemplary description of the methodof computing the user significance pattern is now described in moredetail with respect to FIG. 6. The basic computational steps include

[0132] Gather data on a quantitative scale e.g. collect responses to aquestionnaire on a 7-point scale as generally described above (see FIG.4 for an exemplary question display). 605, 607.

[0133] Normalize responses in individual dimension (correcting responsesfor personal mean and expressing as standard deviations). 607

[0134] Further normalize responses in population dimension (correctingfor population mean and expressing as standard deviations). These valuesare referred to as “double-normalized data”. 609

[0135] Compute aggregate scores for underlying scales based on a scoretable wherein the response to each question is assigned a weight(positive or negative) for each scale. Multiply weights bydouble-normalized data and add the results to get the user's aggregatescore under each scale. 611

[0136] Nine scales (also known as “indexes”) are currently used: 615,617

[0137] OJ=Open-ended/Judgmental

[0138] RS=Risk-Taking/Security-Conscious

[0139] FU=Focused/Unfocused

[0140] CB=Concrete/Abstract

[0141] TP=Territorial/Pacifist

[0142] EM=Empiric/Mythic

[0143] AG=Anomic/Gregarious

[0144] IX=Internal/External Locus of Control

[0145] DH=Deceptive/Honest

[0146] Two additional scales are derived from the mean response (SI) andstandard deviation of response (DW) for each user.

[0147] Scores under each scale are further normalized for the populationmean and standard deviation for that scale. Scores are thereforeexpressed as standard deviations from the population mean.

[0148] Generate archetypes. 619ff An archetype is a set of scores acrossall eleven scales.

[0149] Example of an archetype:

[0150] OJ 2.41

[0151] RS 2.13

[0152] FU 0.65

[0153] CB 1.76

[0154] TP −1.02

[0155] EM −2.37

[0156] AG 0.02

[0157] IX 0.33

[0158] DH 2.45

[0159] SI 1.27

[0160] DW −3.79

[0161] Archetypes are empirically created sets that serve as referencepoints for the natural clustering of such sets (patterns) in any humanpopulation. Traditional statistical tools such as procedure MODECLUS inSAS™ can be used to generate such clusters and arrive at archetypes, orarchetypes may be derived empirically, by trial and error.

[0162] Individual score sets are compared to each archetype by pairwisePearson correlation. 623 The number of archetypes used in such ananalysis will typically range from about six to about twelve. The exactnumber is defined by the operational needs of the analysis. For thepurposes of this embodiment, the number of archetypes needed foranalysis of personal style data is defined as the smallest number ofarchetypes that can generate Pearson correlations of at >0.50 to atleast one of the archetypes, for at least 95% of the population. 625

[0163] The correlations derived to each archetype determine thatindividual's personal style. Further algorithms relate this styleassignment to actual probabilities of behavior or preference, asdescribed hereunder.

[0164] Pearson Correlation Coefficient

[0165] The Pearson Correlation indicated above is described in standardstatistical textbooks such as those referenced above, but forcompleteness is described generally as follows.

[0166] The correlation between two variables reflects the degree towhich the variables are related. The most common measure of correlationis the Pearson Product Moment Correlation (called Pearson's correlationfor short). When measured in a population the Pearson Product Momentcorrelation is designated by the Greek letter rho ((φ). When computed ina sample, it is designated by the letter “r” and is sometimes called“Pearson's r.” Pearson's correlation reflects the degree of linearrelationship between two variables. It ranges from +1 to −1. Acorrelation of +1 means that there is a perfect positive linearrelationship between variables. A correlation of −1 means that there isa perfect negative linear relationship between variables. It would be anegative relationship because high scores on the X-axis would beassociated with low scores on the Y-axis. A correlation of 0 means thereis no linear relationship between the two variables.

[0167] The formula for Pearson's correlation takes on many forms. Acommonly used formula is shown below. The formula looks a bitcomplicated, but taken step by step as shown in the numerical examplebelow, it is really quite simple.$r = \frac{{\sum{XY}} - \frac{\sum{X{\sum Y}}}{N}}{\sqrt{\left( {{\sum X^{2}} - \frac{\left( {\sum X} \right)^{2}}{N}} \right)\quad \left( {{\sum Y^{2}} - \frac{\left( {\sum Y} \right)^{2}}{N}} \right)}}$

[0168] A numerical example is as follows:

[0169] X Y

[0170] 1 2

[0171] 2 5

[0172] 3 6$r = \frac{{\sum{XY}} - \frac{\sum{X{\sum Y}}}{N}}{\sqrt{\left( {{\sum X^{2}} - \frac{\left( {\sum X} \right)^{2}}{N}} \right)\quad \left( {{\sum Y^{2}} - \frac{\left( {\sum Y} \right)^{2}}{N}} \right)}}$

 ΣXY=(1)(2)+(2)(5)+(3)(6)=30

ΣX=1+2+3=6

ΣX ²=1²+2²+3²=14

ΣY=2+5+6=13

ΣY ²=2²+5²+6²=65

N=3

ΣXY−ΣXΣY/N=30−(6)(13)/3=4

ΣX ²−(ΣX)² /N=14−6²/3=2

r=4/{square root}{square root over ((2)(8.6667))}=4/4.16333ΣY ²−(ΣY)²/N=65−13²/3=8.6667=0.9608

[0173] This value, 0.9608, would say that the numbers in the X columnare highly correlated with the numbers in the Y column (a value of +1.0meaning the numbers were perfectly correlated).

[0174] In our example here, if the X column numbers ware derived from auser's inputted answers to three types of questions, and the Y columnwere numbers associated with a specific archetype, then this highcorrelation (0.9608) would characterize this user as highly likely tohave characteristics of this archetype.

[0175] Calculating z Scores

[0176] A simpler looking formula can be used if the numbers areconverted into z scores:

[0177] where z_(x) is the variable X converted into z scores and z_(y)is the variable $r = \frac{\sum{Z_{x}Z_{y}}}{N}$

[0178] Y converted into z scores.

[0179] z scores can be computed as follows:

[0180] The standard normal distribution is a normal distribution with amean of 0 and a standard deviation of 1. Normal distributions can betransformed to standard normal distributions by the formula:

z=(X−μ)/σ

[0181] where X is a score from the original normal distribution, μ isthe mean of the original normal distribution, and a is the standarddeviation of original normal distribution. The standard normaldistribution is sometimes called the z distribution. A z score alwaysreflects the number of standard deviations above or below the mean aparticular score is. For instance, if a person scored a 70 on a testwith a mean of 50 and a standard deviation of 10, then they scored 2standard deviations above the mean. Converting the test scores to zscores, an X of 70 would be:

z=(70−50)/10=2

[0182] So, a z score of 2 means the original score was 2 standarddeviations above the mean. Note that the z distribution will only be anormal distribution if the original distribution (X) is normal.

[0183] The following example illustrates the collection and analysis ofPTT data from a large human sample, and how correlations weresuccessfully made between archetypes and individual preferences foroutdoor activities.

[0184] Data were collected anonymously from a cohort of 1373 adults (69%female) using two types of online survey questionnaires for eachindividual. Psychometric data were collected from a timed-responsequestionnaire, with responses to fifty statements collected on aseven-point scale (Strongly Disagree, Disagree, Somewhat Disagree,Neutral, Somewhat Agree, Agree, Strongly Agree). These statements wereselected out of an original inventory of 83 statements, based on aseries of beta-tests designed to validate items in the inventory throughfactor analysis and other conventional methods. The original inventorywas compiled from statements adapted from previously validated, publicdomain, personality test questionnaires (Robinson, et al, 1991). Basedon previous work in these areas by other investigators, statements weredesigned to elicit responses related to novelty-seeking, risk-taking,ability to focus, abstractive thinking, competitiveness, empiricism,social status-seeking, independence, extraversion, response bias anddecisiveness. Initial scoring matrices were compiled and refined asfollows:

[0185] (a) raw answers (−3 to +3) were normalized for the user's ownmean and SD from mean. The validity of this correction factor wasconfirmed by asking the same respondents to answer an unrelated set of50 statements. The means and SDs correlated better than 0.97 for allusers, when compared pairwise.

[0186] (b) answers were further normalized for each question, using thepopulation mean and SD for that question.

[0187] (c) normalized answers were used to compute aggregate scores foreach trait based on the initial scoring matrix. The final scores werecompared by pairwise correlation to the normalized answers for eachquestion. The resulting values were then used to adjust the scoringmatrix so that those responses to questions that correlated moststrongly to the construct being scored, counted proportionately more forthat construct.

[0188] (d) the process of adjusting the scoring matrix was performediteratively 4-7 times until successive iterations agreed to within onepercent.

[0189] Clustering of respondents was accomplished in two steps:

[0190] First, the trait scores of 1373 participants were analyzed usingstandard Principal Component Analysis (SAS Proc PRINCOMP). The techniquereduced the true dimensionality of the data space to three or fourdimensions (Scree plot analysis), with over 80% of the variance in thedata being accounted for in the first two Principal Components. Thefirst Principal Component consisted mainly of measures that describenovelty-seeking while the second was composed of those that describecompetitiveness. The data were then clustered hierarchically using SASprocedure MODECLUS.

[0191] Eight clusters which together account for 94.3% of the population(these were the only clusters containing at least 2% of the populationof 1373 individuals) were identified in the data. A plot of these eightclusters using the two largest Principal Components, which togetheraccount for over 80% of the variability in the population, is shownabove.

[0192] In order to lay the foundation for the identification ofarchetypes, for each of these clusters the average scores werecalculated for all eleven trait variables. By simple pairwisecorrelation, any individual's 11-score set can be compared to eacharchetype. By this method, it was possible to assign most of theindividuals in the population to archetypes based on Pearson correlationvalues >0.6 to at least one archetype. However, the assignment could bemade substantially more discriminating by making small heuristicadjustments to the archetypes. We are now investigating why theseadjustments were effective, in order to derive a formal method formaking such adjustments in the future. TABLE II Chi-Square Test ofPersonality Clusters Versus Recreational Activities* RecreationalActivity p Outdoor Activities 0.063 Sports 0.001** Books & Music 0.045**Surfing The Net 0.087 Social Activities 0.110 Movies & TV 0.243 CreativeActivities 0.018**

[0193] In order to lay the foundation for predictive algorithms linkingclusters to behavior, a chi-square test was used to see if the hobbiesand recreational activities enjoyed by these individuals also grouped inthese clusters. The null hypothesis is that the frequency of peopleenjoying a particular form of recreational activity in a given clusteris similar to that observed in the population as a whole. Our initialresults (Table II) show, for example, that in almost every recreationalactivity (e.g., outdoor recreation, sports, reading books, socialpursuits, etc.) a significant (p<0.05) or marginally significant(p<0.10) discordance of pattern across the clusters was observed. Other,more sophisticated analyses, such as canonical correlation morecompletely describe these kinds of relationships, but these data are notincluded here, as they are quite extensive. In general, they support astrong connection between cognitive clusters and professional,aesthetic, learning and recreational behaviors.

[0194] One skilled in the art will recognize that variations on how thepsychological test is presented may be done. For example, instead of aquestion and answer way of obtaining response from a user, thepsychological test may be presented via a game embodiment. In addition,variations of the questions or types of questions may be employed in theinvention.

[0195]FIG. 5 illustrates a high-level block diagram showing targetedmarketing based on the user's significance pattern. In the first step,as shown in 502, the user logs onto the system by accessing the Websiteand entering the correct user name and password. Once the user logs on,the system retrieves the user's significance pattern, e.g., the mnemoniccode “MAU9R.” One skilled in the art will recognize that otherinformation about the user may also be retrieved from the correspondingdatabase. After the system retrieves the significance pattern, thesystem may target the user at step 506, e.g., by showing ads on the Webpage that would likely interest the user. This may be implemented byhaving the system show only search results that matches the user'sarchetype and characteristics, such as retrieving products which areclassified for ARTISTS (i.e., contain a “yes” on the artist field). Ageneralized exemplary flow diagram of the matching of target informationto the user's classification significance pattern is shown in FIG. 7.

[0196] One skilled in the art will recognize that other uses of theuser's significance pattern may be employed. For example, a chat roomcategorized by archetype may be created thus enabling users of similarpersonality to chat with each other.

[0197] Those skilled in the art will recognize that the method andproduct of the present invention has many industrial applications,particularly in web-enabled e-commerce, and the present invention is notlimited to the representative embodiments described herein. Allmodifications, variations, or equivalent arrangements andimplementations that are within the scope of the attached claims shouldtherefore be considered within the scope of the invention.

[0198] It should be understood that all of the computers of the systemsillustrated in FIG. 1 preferably have a construction similar to thatshown in FIG. 2, so that details described with respect to the FIG. 2computer 200 will be understood to apply to all computers of the systemsin FIG. 1. Any of the computers can have an alternative construction, solong as they can support the functionality described herein.

[0199] One skilled in the art will also recognize that variations in thesteps, as well as the order of execution, may be done and still make theinvention operate in accordance with the features of the invention.

I claim:
 1. A computer implemented method for matching a computer userwith target information comprising the acts of; a) creating aclassification significance pattern for a user by using a psychologicaltest wherein at least some of the user's answers to test questions areused to derive the classification significance pattern for the user; b)creating a classification index for the target information wherein theclassification index can be matched to one or more elements of theclassification significance pattern of the user; and c) finding targetdata whose classification index matches one or more elements of theclassification significance pattern of the user.
 2. The computerimplemented method of claim 1 wherein the user does not make priorexplicit disclosure of interest in the target information.
 3. Thecomputer implemented method of claim 1 wherein the classificationsignificance pattern contains data indicative of an archetype to whichthe user corresponds.
 4. The computer implemented method of claim 1wherein the classification index of the target information contains datarelating to an archetype to which the user may correspond.
 5. Thecomputer implemented method of claim 1 wherein the target information isselected from one or more of the following categories: job placement,opinion surveys, dating/matching services, travel, sports,entertainment, financial, biomedical, computers, software andnetworking.
 6. The computer implemented method of claim 1 wherein thepsychological test used is a PTT.
 7. The computer implemented method ofclaim 1 comprising the additional act of receiving a request for targetinformation from a user via the Internet or a data network wherein therequest is made anonymously via a pseudonym.
 8. A computer implementedmethod for matching a computer user with target information comprisingthe acts of; a) creating a classification significance pattern for theuser by using a psychological test wherein at least some of the user'sanswers to test questions are used to derive the classificationsignificance pattern for the user, and wherein the classificationsignificance pattern contains data indicative of an archetype to whichthe user corresponds; b) creating a classification index for the targetinformation wherein the classification index can be matched to one ormore elements of the classification significance pattern of the user;and c) finding target data whose classification index matches one ormore elements of the classification significance pattern of the user. 9.A computer implemented method for matching a computer user with targetinformation comprising the acts of; a) creating a classificationsignificance pattern for the user by using a psychological test whereinat least some of the user's answers to test questions are used to derivethe classification significance pattern for the user, and wherein theclassification significance pattern contains data indicative of anarchetype to which the user corresponds; b) creating a classificationindex for the target information wherein the classification index can bematched to one or more elements of the classification significancepattern of the user, and wherein the classification index of the targetinformation contains data indicative of an archetype to which the usermay correspond; and c) finding target data whose classification indexmatches one or more elements of the classification significance patternof the user.
 10. The computer implemented method of claim 9 wherein theuser does not make prior explicit disclosure of interest in the targetinformation.
 11. The computer implemented method of claim 9 wherein thetarget information is selected from one or more of the followingcategories: job placement, opinion surveys, dating/matching services,travel, sports, entertainment, financial, biomedical, computers,software and networking.
 12. The computer implemented method of claim 9wherein the psychological test used is a PTT.
 13. The computerimplemented method of claim 9 comprising the additional act of receivinga request for target information from a user via the Internet or a datanetwork wherein the request is made anonymously via a pseudonym.
 14. Acomputer implemented method for matching a computer user with targetinformation comprising the acts of; a) creating a classificationsignificance pattern for the user by using a psychological test whereinat least some of the user's answers to test questions are used to derivethe classification significance pattern for the user, and wherein theclassification significance pattern contains data indicative of anarchetype to which the user corresponds; b) creating a classificationindex for the target information wherein the classification index can bematched to one or more elements of the classification significancepattern of the user, and wherein the classification index of the targetinformation contains data relating to an archetype to which the user maycorrespond; c) finding target data whose classification index matchesone or more elements of the classification significance pattern of theuser; and d) wherein the target information is selected from one or moreof the following categories: job placement, opinion surveys,dating/matching services, travel, sports, entertainment, financial,biomedical, computers, software and networking.
 15. The computerimplemented method of claim 14 wherein the user does not make priorexplicit disclosure of interest in the target information.
 16. Thecomputer implemented method of claim 14 wherein the psychological testused is a PTT.
 17. The computer implemented method of claim 14comprising the additional act of receiving a request for targetinformation from a user via the Internet or a data network wherein therequest is made anonymously via a pseudonym.
 18. A computer implementedmethod for matching a computer user with target information comprisingthe acts of: a) receiving a request for target information from a uservia the Internet or a data network; b) retrieving from a data base, aclassification significance pattern for the user, wherein theclassification significance pattern for the user was created by using atleast some of the user's answers to test questions from a psychologicaltest, and wherein the classification significance pattern contains dataindicative of an archetype to which the user corresponds; c) using theclassification significance pattern for the user to search a data baseof target information; and d) displaying for the user target informationwhich matches one or more elements of the classification significancepattern for the user.
 19. The computer implemented method of claim 18wherein the user does not make prior explicit disclosure of interest inthe target information.
 20. The computer implemented method of claim 18wherein the psychological test used is a PTT.
 21. An apparatus formatching a computer user with target information comprising: a) acomputer server node in a network system, the computer server nodehaving a first code mechanism configured to create a classificationsignificance pattern for a user by using a psychological test wherein atleast some of the user's answers to test questions are used to derivethe classification significance pattern for the user; b) a data basesystem electronically coupled to the server node containing targetinformation, the target information having at least one classificationindex for the target information wherein the classification index can bematched to one or more elements of the classification significancepattern of the user; and c) a second code mechanism electronicallycoupled to the first code mechanism configured to find target data whoseclassification index matches one or more elements of the classificationsignificance pattern of the user.
 22. The apparatus for matching acomputer user with target information of claim 21 wherein theclassification significance pattern contains data indicative of anarchetype to which the user corresponds.
 23. The apparatus for matchinga computer user with target information of claim 21 wherein theclassification index of the target information contains data indicativeof an archetype to which the user may correspond.
 24. The apparatus formatching a computer user with target information of claim 21 wherein thetarget information is selected from one or more of the followingcategories: job placement, opinion surveys, dating/matching services,travel, sports, entertainment, financial, biomedical, computers,software and networking.
 25. The apparatus for matching a computer userwith target information of claim 21 wherein the user can access thecomputer server node from a client computer device via the Internet. 26.The apparatus for matching a computer user with target information ofclaim 21 wherein the user can access the computer server node from amobile computer communications device.
 27. The apparatus for matching acomputer user with target information of claim 21 wherein the user canaccess the computer server node anonymously via a pseudonym.
 28. Anapparatus for matching a computer user with target informationcomprising: a) a computer server node in a network system, the computerserver node having a first code mechanism configured to create aclassification significance pattern for a user by using a psychologicaltest wherein at least some of the user's answers to test questions areused to derive the classification significance pattern for the user, andwherein the classification significance pattern contains data indicativeof an archetype to which the user corresponds; b) a data base systemelectronically coupled to the server node containing target information,the target information having at least one classification index for thetarget information wherein the classification index can be matched toone or more elements of the classification significance pattern of theuser; and c) a second code mechanism electronically coupled to the firstcode mechanism configured to find target data whose classification indexmatches one or more elements of the classification significance patternof the user.
 29. The apparatus of claim 28 wherein the user does notmake a prior explicit disclosure of interest in the target information.30. The apparatus of claim 28 wherein the target information is selectedfrom one or more of the following categories: job placement, opinionsurveys, dating/matching services, travel, sports, entertainment,financial, biomedical, computers, software and networking.
 31. Theapparatus of claim 28 wherein the psychological test used is a PTT. 32.The apparatus of claim 28 wherein the computer server node receives arequest for target information from a user via the Internet or a datanetwork
 33. An apparatus for matching a computer user with targetinformation comprising: a) a client computer terminal for use inaccessing at least one server system in a network, wherein the at leastone server system can try to match a classification significance patternfor a user with a classification index associated with the targetinformation, and wherein the classification significance pattern for theuser was generated by using a psychological test wherein at least someof the user's answers to test questions are used to derive theclassification significance pattern for the user; and b. a displaydevice coupled electronically to the client computer terminal which candisplay for the user any matching target information which the at leastone server system may have detected.
 34. A computer program productembedded in a computer readable memory for matching a computer user withtarget information comprising: a) a first code mechanism configured tocreate a classification significance pattern for a user by using apsychological test wherein at least some of the user's answers to testquestions are used to derive the classification significance pattern forthe user, and wherein the classification significance pattern containsdata indicative of an archetype to which the user corresponds; b) asecond code mechanism electronically coupled to the first code mechanismconfigured to create a classification index for the target informationwherein the classification index can be matched to one or more elementsof the classification significance pattern of the user; and c) a thirdcode mechanism electronically coupled to the first code mechanismconfigured to find target data whose classification index matches one ormore elements of the classification significance pattern of the user.35. A computer program product residing on a computer readable mediumfor matching a computer user with target information comprising: a) afirst code mechanism on a client computer terminal for use in accessingat least one server system in a network, wherein the at least one serversystem can try to match a classification significance pattern for a userwith a classification index associated with the target information, andwherein the classification significance pattern for the user wasgenerated by using a psychological test wherein at least some of theuser's answers to test questions are used to derive the classificationsignificance pattern for the user; and b. a second code mechanism in adisplay device coupled electronically to the client computer terminalwhich can display for the user any matching target information which theat least one server system may have detected.
 36. The computer programproduct of claim 35 wherein the user does not make a prior explicitdisclosure of interest in the target information.
 37. The computerprogram product of claim 35 wherein the target information is selectedfrom one or more of the following categories: job placement, opinionsurveys, dating/matching services, travel, sports, entertainment,financial, biomedical, computers, software and networking.
 38. Thecomputer program product of claim 35 wherein the psychological test usedis a PTT.
 39. An apparatus for matching a computer user with targetinformation comprising: a) a computer server node in a network system,the computer server node providing access to a computer user desiringtarget information; b) a first data base electronically coupled to theserver node containing a classification significance pattern for thecomputer user, wherein the classification significance pattern wascreated by using a psychological test wherein at least some of thecomputer user's answers to test questions are used to derive theclassification significance pattern for the computer user, and whereinthe classification significance pattern contains data indicative of anarchetype to which the computer user corresponds; b) a second data baseelectronically coupled to the server node containing target information,the target information having at least one classification index for thetarget information wherein the classification index can be matched toone or more elements of the classification significance pattern of theuser; c) a first code mechanism in the server node electronicallycoupled to the first data base and to the second data base, the firstcode mechanism configured to find target data whose classification indexmatches one or more elements of the classification significance patternof the user; and d) a second code mechanism coupled electronically tothe first code mechanism configured to display to the user any targetinformation found to match one or more elements of the classificationsignificance pattern of the user.
 40. An apparatus for providing a userwith target information comprising: a) means for communicatingelectronically with the user; b) means for relating a classificationsignificance pattern to the user, wherein the classificationsignificance pattern was created for the user by using a psychologicaltest wherein at least some of the user's answers to test questions wereused to derive the classification significance pattern for the user, andwherein the classification significance pattern contains data indicativeof an archetype to which the user corresponds; c) means for searching adata base containing target information, the target information havingat least one classification index which can be matched to one or moreelements of the classification significance pattern of the user; and d)means for displaying matching target information to the user.
 41. Amethod for matching users with target information where said users donot make prior explicit disclosure of interest in said targetinformation, comprising: a) providing a psychological test forgenerating an abstract classification significance pattern from userresponses where said psychological test measures a set ofclassifications; b) administering said psychological test; c) receivinguser's responses to said psychological test; d) generating an abstractclassification significance pattern based on said user's responses; ande) using said classification significance pattern to predict a user'sinterest in target information.
 42. A method as defined in claim 41where said abstract classification significance pattern is not based ondemographic attributes.
 43. A method as defined in claim 41 where saidtarget information is selected from one or more of the followingcategories: job placement, opinion surveys, dating/matching services,travel, sports, entertainment, financial, biomedical, computers,software and networking.
 44. A method as defined in claim 41 where saidpsychological test is a PTT.